Causal BERT : Language models for causality detection between events expressed in text

Understand

Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance.

• On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like Twitter, dedicated to communicating and exploring various types of causality in the real world.
• Recognizing these "Cause-Effect" relationships between natural language events continues to remain a challenge simply because it is often expressed implicitly.
• Implicit causality is hard to detect through most of the techniques employed in literature and can also, at times be perceived as ambiguous or vague.